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Flanker Task-Based VHDR Dataset Analysis for Error Rate Prediction

Publication Type : Conference Paper

Publisher : Springer, Singapore

Source : Springer, Singapore

Url :

School : School of Engineering

Center : Humanitarian Technology (HuT) Labs

Year : 2022

Abstract : The human brain is the most efficient computer which works much faster than the human-made computers. But, when it comes to computation and logical things, computers show more efficiency. Thus, BCI (Brain-Computer Interface) has many advantages and can be a reason for a new technological evolution. In this paper, we are trying to use this technology as a tool to do some effective analysis. A system is introduced that shows how well a person can do the flanker task without making an error using the EEG signals recorded while the person performing the task. We use the machine learning algorithm LDA (Linear Discriminant Analysis) and the windowed means paradigm which is used for observing slow-changing brain dynamics mainly the ERP (Event-Related Potential). In our experiment, we use the data of two identical twins for the analysis. MATLAB with the BCILAB plugin is used as the platform for the analysis. Once the analysis is done, we can get the error rate of a person performing the task. We used this as a tool to increase the concentration levels and give the person feedback after every trial.

Cite this Research Publication : Rajesh Kannan Megalingam, Sankardas Kariparambil Sudheesh, Vamsy Vivek Gedela "Flanker Task-Based VHDR Dataset Analysis for Error Rate Prediction " 2022

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